Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research.

翻译：个性化头颈癌治疗方案显著提高了患者的生存率，但常导致研究不足的长期症状，这些症状会严重影响生活质量。顺序规则挖掘是一种有前景的无监督机器学习方法，用于预测时间序列数据中的纵向模式，但该方法可能输出大量重复模式，若无可视化分析辅助则难以解读。我们提出了一种数据驱动的人机协同分析可视化系统，该系统与癌症症状研究领域的顺序规则挖掘模型构建者合作开发，旨在促进大规模多元队列症状数据中的机制性知识发现。本系统支持基于治疗中症状对治疗后症状进行多元预测建模，通过集成顺序规则挖掘、聚类与聚合的后端，以及用于开发与调优预测模型的定制化前端实现这一目标。该系统还能在个性化护理中典型治疗决策的背景下解释所得预测结果。我们与跨学科建模者及头颈肿瘤研究人员小组对模型与系统进行了评估，结果表明本系统能有效支持临床与症状研究。